Skip to main navigation
Skip to search
Skip to main content
Illinois Experts Home
LOGIN & Help
Home
Profiles
Research units
Research & Scholarship
Datasets
Honors
Press/Media
Activities
Search by expertise, name or affiliation
Deep Learning Sequence Methods in Multiphysics Modeling of Steel Solidification
Seid Koric
,
Diab W. Abueidda
National Center for Supercomputing Applications (NCSA)
Mechanical Science and Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Deep Learning Sequence Methods in Multiphysics Modeling of Steel Solidification'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Sequence Method
100%
Multiphysics Modeling
100%
Deep Learning
100%
Steel Solidification
100%
Learning Sequence
100%
Multiphysics
50%
Temperature Effect
50%
Phase Fraction
50%
Highly Nonlinear
50%
History Dependence
50%
Deep Learning Methods
50%
Austenite
50%
Thermomechanical Behavior
50%
Training Data
50%
Numerical Modeling
50%
Loading History
50%
Modeling Data
50%
High Performance Computing
50%
Ferrite
50%
Testing Data
50%
Continuous Caster
50%
Sequence Deep Learning
50%
Deep Learning Network
50%
Material Science
Solidification
100%
Mechanical Property
100%
Austenite
100%